Authors:
Hongqiang Mo
1
;
Zhong Li
2
and
Qiliang Du
1
Affiliations:
1
South China University of Technology, China
;
2
FernUniversitaet in Hagen, Germany
Keyword(s):
Building Block, Encoding, Genetic Algorithm, Schema Processing, Series Expansion.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Computational Intelligence
;
Evolutionary Computing
;
Genetic Algorithms
;
Informatics in Control, Automation and Robotics
;
Intelligent Control Systems and Optimization
;
Representation Techniques
;
Soft Computing
Abstract:
In line with the theory of schema sampling, a hypothesis could be made that sufficient supply of loworder
building blocks (BBs) was one of the necessary conditions for a genetic algorithm(GA) to work.
A consequential question of this hypothesis regards, when a certain fitness function is optimized with a
commonly used GA, whether it is rare or common that there are plenty of low-order BBs. It is remarked
that, when a base-m encoded GA is applied to a fitness function that is linearly combined of sinusoidal basis
functions with integral frequencies, it is unlikely to obtain order-1 BBs with fixed positions at multiple loci,
i.e., it is rare that there are plenty of order-1 BBs. However, if a considerable part of the sinusoidal basis
functions are with frequencies exponential to a positive integer m, a base-m encoding can provide relatively
more order-1 BBs compared with the encodings with cardinalities other than m.